def get_opt_model(x,y):
# grid search and SVM
clf = svm.SVC(kernel='rbf', class_weight='auto')
clf.probability = True
#clf = svm.SVC(kernel='rbf')
clf, best_score = plib.grid_search(clf, x, y, n_folds=10, verbose=False)
clf.fit(x,y)
return clf
def get_opt_model(x, y):
# grid search and SVM
clf = svm.SVC(kernel='rbf', class_weight='auto')
clf.probability = True
#clf = svm.SVC(kernel='rbf')
clf, best_score = plib.grid_search(clf, x, y, n_folds=10, verbose=False)
clf.fit(x, y)
return clf
def compute_acc_conf(x,y,confounds,verbose=False,balanced=True,loo=False,nfolds=10,gs_kfolds=5,optimize=True,C=.01):
encoder = preprocessing.LabelEncoder()
encoder.fit(y)
# remove intra matrix mean and var
#x = ts.normalize_data(x)
#cv = cross_validation.KFold(len(y),n_folds=10)
if loo:
cv = cross_validation.LeaveOneOut(len(y))
else:
cv = StratifiedKFold(y=encoder.transform(y), n_folds=nfolds)
mean_tpr = 0.0
mean_fpr = np.linspace(0, 1, 100)
all_tpr = []
total_test_score=[]
y_pred=[]
#clf_array = []
bc_all = []
prec = []
recall = []
if len(np.unique(y))==1:
print 'Unique class: 100%', np.sum(encoder.transform(y)==0)/len(y)
return (1., 0.,len(y))
for i, (train, test) in enumerate(cv):
select_x = x.copy()
#betacluster = bc.BetaCluster(crm.transform(confounds[train,:],select_x[train,:]),encoder.transform(y[train]),100,k_feature=200)
#bc_all.append(betacluster)
if balanced:
clf = SVC(kernel='linear', class_weight='auto', C=C)
else:
clf = SVC(kernel='linear',C=C)
if len(confounds) == 0:
xtrain = select_x[train,:]
xtest = select_x[test,:]
else:
crm = ConfoundsRm(confounds[train,:],select_x[train,:])
xtrain = crm.transform(confounds[train,:],select_x[train,:])
xtest = crm.transform(confounds[test,:],select_x[test,:])
ytrain = encoder.transform(y[train])
ytest = encoder.transform(y[test])
#clf.probability = True
if optimize:
clf, score = plib.grid_search(clf, xtrain,ytrain, n_folds=gs_kfolds, verbose=verbose)
clf.fit(xtrain,ytrain)
total_test_score.append( clf.score(xtest,ytest))
#clf_array.append(clf)
prec.append(metrics.precision_score(ytest, clf.predict(xtest)))
recall.append(metrics.recall_score(ytest, clf.predict(xtest)))
if loo:
y_pred.append(clf.predict(xtest))
if verbose:
print('nSupport: ',clf.n_support_)
print "Train:",clf.score(xtrain,ytrain)
print "Test :",clf.score(xtest,ytest)
print "Prediction :",clf.predict(xtest)
print "Real Labels:",ytest
print('Precision:',prec[-1],'Recall:',recall[-1])
y_pred = np.array(y_pred)[:,0]
if loo:
total_std_test_score = estimate_std(metrics.accuracy_score(encoder.transform(y), np.array(y_pred)),len(y))
print('Mean:', np.mean(total_test_score),'Std:', total_std_test_score,'AvgPrecision:',np.mean(prec),'AvgRecall:',np.mean(recall) )
return [np.mean(total_test_score), total_std_test_score, len(y),y_pred]
else:
print('Mean:', np.mean(total_test_score),'Std:', np.std(total_test_score),'AvgPrecision:',np.mean(prec),'AvgRecall:',np.mean(recall) )
return [np.mean(total_test_score), np.std(total_test_score),len(y)]
def compute_acc_conf(x,
y,
confounds,
verbose=False,
balanced=True,
loo=False,
optimize=True,
C=.01):
encoder = preprocessing.LabelEncoder()
encoder.fit(y)
# remove intra matrix mean and var
#x = ts.normalize_data(x)
#cv = cross_validation.KFold(len(y),n_folds=10)
if loo:
cv = cross_validation.LeaveOneOut(len(y))
else:
cv = StratifiedKFold(y=encoder.transform(y), n_folds=10)
mean_tpr = 0.0
mean_fpr = np.linspace(0, 1, 100)
all_tpr = []
total_test_score = []
y_pred = []
#clf_array = []
bc_all = []
prec = []
recall = []
if len(np.unique(y)) == 1:
print 'Unique class: 100%', np.sum(encoder.transform(y) == 0) / len(y)
return (1., 0., len(y))
for i, (train, test) in enumerate(cv):
select_x = x.copy()
#betacluster = bc.BetaCluster(crm.transform(confounds[train,:],select_x[train,:]),encoder.transform(y[train]),100,k_feature=200)
#bc_all.append(betacluster)
if balanced:
clf = SVC(kernel='linear', class_weight='auto', C=C)
else:
clf = SVC(kernel='linear', C=C)
if len(confounds) == 0:
xtrain = select_x[train, :]
xtest = select_x[test, :]
else:
crm = ConfoundsRm(confounds[train, :], select_x[train, :])
xtrain = crm.transform(confounds[train, :], select_x[train, :])
xtest = crm.transform(confounds[test, :], select_x[test, :])
ytrain = encoder.transform(y[train])
ytest = encoder.transform(y[test])
#clf.probability = True
if optimize:
clf, score = plib.grid_search(clf,
xtrain,
ytrain,
n_folds=10,
verbose=verbose)
clf.fit(xtrain, ytrain)
total_test_score.append(clf.score(xtest, ytest))
#clf_array.append(clf)
prec.append(metrics.precision_score(ytest, clf.predict(xtest)))
recall.append(metrics.recall_score(ytest, clf.predict(xtest)))
if loo:
y_pred.append(clf.predict(xtest))
if verbose:
print('nSupport: ', clf.n_support_)
print "Train:", clf.score(xtrain, ytrain)
print "Test :", clf.score(xtest, ytest)
print "Prediction :", clf.predict(xtest)
print "Real Labels:", ytest
print('Precision:', prec[-1], 'Recall:', recall[-1])
if loo:
total_std_test_score = estimate_std(
metrics.accuracy_score(encoder.transform(y), np.array(y_pred)),
len(y))
print('Mean:', np.mean(total_test_score), 'Std:', total_std_test_score,
'AvgPrecision:', np.mean(prec), 'AvgRecall:', np.mean(recall))
return (np.mean(total_test_score), total_std_test_score, len(y))
else:
print('Mean:', np.mean(total_test_score), 'Std:',
np.std(total_test_score), 'AvgPrecision:', np.mean(prec),
'AvgRecall:', np.mean(recall))
return (np.mean(total_test_score), np.std(total_test_score))